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Anomaly detection or outlier detection is a common task in various domains,which has attracted significant research efforts in recent years. Existingworks mainly focus on structured data such as numerical or categorical data;however, anomaly detection on unstructured textual data is less attended. Inthis work, we target the textual anomaly detection problem and propose a deepanomaly-injected support vector data description (AI-SVDD) framework. AI-SVDDnot only learns a more compact representation of the data hypersphere but alsoadopts a small number of known anomalies to increase the discriminative power.To tackle text input, we employ a multilayer perceptron (MLP) network inconjunction with BERT to obtain enriched text representations. We conductexperiments on three text anomaly detection applications with multipledatasets. Experimental results show that the proposed AI-SVDD is promising andoutperforms existing works.

Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection